{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djUvWu41mtXa"
      },
      "source": [
        "##### Copyright 2020 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "su2RaORHpReL"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NztQK2uFpXT-"
      },
      "source": [
        "# Accessing TensorBoard Data as DataFrames"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eDXRFe_qp5C3"
      },
      "source": [
        "## Overview\n",
        "\n",
        "The main feature of TensorBoard is its interactive GUI. However, users sometimes want to **programmatically** read the data logs stored in TensorBoard, for purposes such as performing post-hoc analyses and creating custom visualizations of the log data.\n",
        "\n",
        "TensorBoard 2.3 supports this use case with `tensorboard.data.experimental.ExperimentFromDev()`. It allows programmatic access to TensorBoard's [scalar logs](https://www.tensorflow.org/tensorboard/scalars_and_keras). This page demonstrates the basic usage of this new API.\n",
        "\n",
        "> **Note:**\n",
        "> 1. This API is still in its experimental stage, as reflected by its API namespace. This means the API may be subject to breaking changes in the future.\n",
        "> 2. Currently, this feature supports only logdirs uploaded to TensorBoard.dev, a free hosted service for persisting and sharing your TensorBoard. Support for locally stored TensorBoard logdir will be added in the future. Briefly, you can upload a TensorBoard logdir on you local filesystem to TensorBoard.dev with a single line of command: `tensorboard dev upload --logdir <logdir>`. See the documentation at [tensorboard.dev](https://tensorboard.dev) for more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6E4sB4Qulnz"
      },
      "source": [
        "## Setup\n",
        "\n",
        "In order to use the programmatic API, make sure you install `pandas` alongside `tensorboard`.\n",
        "\n",
        "We'll use `matplotlib` and `seaborn` for custom plots in this guide, but you can choose your preferred tool to analyze and visualize `DataFrame`s."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dG-nnZK9qW9z"
      },
      "outputs": [],
      "source": [
        "!pip install tensorboard pandas\n",
        "!pip install matplotlib seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3U5gdCw_nSG3"
      },
      "outputs": [],
      "source": [
        "from packaging import version\n",
        "\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "import tensorboard as tb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "1qIKtOBrqc9Y"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorBoard version:  2.3.0a20200626\n"
          ]
        }
      ],
      "source": [
        "major_ver, minor_ver, _ = version.parse(tb.__version__).release\n",
        "assert major_ver >= 2 and minor_ver >= 3, \\\n",
        "    \"This notebook requires TensorBoard 2.3 or later.\"\n",
        "print(\"TensorBoard version: \", tb.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-aYbmaS74Xs"
      },
      "source": [
        "## Loading TensorBoard scalars as a `pandas.DataFrame`\n",
        "\n",
        "Once a TensorBoard logdir has been uploaded to TensorBoard.dev, it becomes what we refer to as an *experiment*. Each experiment has a unique ID, which can be found in the TensorBoard.dev URL of the experiment. For our demonstration below, we will use a TensorBoard.dev experiment at:\n",
        "https://tensorboard.dev/experiment/c1KCv3X3QvGwaXfgX1c4tg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "S39rRajbyOqc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>run</th>\n",
              "      <th>tag</th>\n",
              "      <th>step</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>epoch_accuracy</td>\n",
              "      <td>0</td>\n",
              "      <td>0.966867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>epoch_accuracy</td>\n",
              "      <td>1</td>\n",
              "      <td>0.986283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>epoch_accuracy</td>\n",
              "      <td>2</td>\n",
              "      <td>0.989333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>epoch_accuracy</td>\n",
              "      <td>3</td>\n",
              "      <td>0.991933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>epoch_accuracy</td>\n",
              "      <td>4</td>\n",
              "      <td>0.991733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1195</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>epoch_loss</td>\n",
              "      <td>15</td>\n",
              "      <td>0.020157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1196</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>epoch_loss</td>\n",
              "      <td>16</td>\n",
              "      <td>0.020212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1197</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>epoch_loss</td>\n",
              "      <td>17</td>\n",
              "      <td>0.020364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1198</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>epoch_loss</td>\n",
              "      <td>18</td>\n",
              "      <td>0.022192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1199</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>epoch_loss</td>\n",
              "      <td>19</td>\n",
              "      <td>0.032140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1200 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       run             tag  step     value\n",
              "0         adam,run_1/train  epoch_accuracy     0  0.966867\n",
              "1         adam,run_1/train  epoch_accuracy     1  0.986283\n",
              "2         adam,run_1/train  epoch_accuracy     2  0.989333\n",
              "3         adam,run_1/train  epoch_accuracy     3  0.991933\n",
              "4         adam,run_1/train  epoch_accuracy     4  0.991733\n",
              "...                    ...             ...   ...       ...\n",
              "1195  sgd,run_5/validation      epoch_loss    15  0.020157\n",
              "1196  sgd,run_5/validation      epoch_loss    16  0.020212\n",
              "1197  sgd,run_5/validation      epoch_loss    17  0.020364\n",
              "1198  sgd,run_5/validation      epoch_loss    18  0.022192\n",
              "1199  sgd,run_5/validation      epoch_loss    19  0.032140\n",
              "\n",
              "[1200 rows x 4 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "experiment_id = \"c1KCv3X3QvGwaXfgX1c4tg\"\n",
        "experiment = tb.data.experimental.ExperimentFromDev(experiment_id)\n",
        "df = experiment.get_scalars()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzF2c0QFTCB5"
      },
      "source": [
        "`df` is a [`pandas.DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) that contains all scalar logs of the experiment.\n",
        "\n",
        "The columns of the `DataFrame` are:\n",
        "- `run`: each run corresponds to a subdirectory of the original logdir. In this experiment, each run is from  a complete training of a convolutional neural network (CNN) on the MNIST dataset with a given optimizer type  (a training hyperparameter). This `DataFrame` contains multiple such runs, which correspond to repeated training runs under different optimizer types.\n",
        "- `tag`: this describes what the `value` in the same row means, that is, what metric the value represents in the row. In this experiment, we have only two unique tags: `epoch_accuracy` and `epoch_loss` for the accuracy and loss metrics respectively.\n",
        "- `step`: This is a number that reflects the serial order of the corresponding row in its run. Here `step` actually refers to epoch number. If you wish to obtain the timestamps in addition to the `step` values, you can use the keyword argument `include_wall_time=True` when calling `get_scalars()`.\n",
        "- `value`: This is the actual numerical value of interest. As described above, each `value` in this particular `DataFrame` is either a loss or an accuracy, depending on the `tag` of the row."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "fpsCq3_uf37q"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['adam,run_1/train' 'adam,run_1/validation' 'adam,run_2/train'\n",
            " 'adam,run_2/validation' 'adam,run_3/train' 'adam,run_3/validation'\n",
            " 'adam,run_4/train' 'adam,run_4/validation' 'adam,run_5/train'\n",
            " 'adam,run_5/validation' 'rmsprop,run_1/train' 'rmsprop,run_1/validation'\n",
            " 'rmsprop,run_2/train' 'rmsprop,run_2/validation' 'rmsprop,run_3/train'\n",
            " 'rmsprop,run_3/validation' 'rmsprop,run_4/train'\n",
            " 'rmsprop,run_4/validation' 'rmsprop,run_5/train'\n",
            " 'rmsprop,run_5/validation' 'sgd,run_1/train' 'sgd,run_1/validation'\n",
            " 'sgd,run_2/train' 'sgd,run_2/validation' 'sgd,run_3/train'\n",
            " 'sgd,run_3/validation' 'sgd,run_4/train' 'sgd,run_4/validation'\n",
            " 'sgd,run_5/train' 'sgd,run_5/validation']\n",
            "['epoch_accuracy' 'epoch_loss']\n"
          ]
        }
      ],
      "source": [
        "print(df[\"run\"].unique())\n",
        "print(df[\"tag\"].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAC-w8W5quOr"
      },
      "source": [
        "## Getting a pivoted (wide-form) DataFrame\n",
        "\n",
        "In our experiment, the two tags (`epoch_loss` and `epoch_accuracy`) are present at the same set of steps in each run. This makes it possible to obtain a \"wide-form\" `DataFrame` directly from `get_scalars()` by using the `pivot=True` keyword argument. The wide-form `DataFrame` has all its tags included as columns of the DataFrame, which is more convenient to work with in some cases including this one.\n",
        "\n",
        "However, beware that if the the condition of having uniform sets of step values across all tags in all runs is not met, using `pivot=True` will result in an error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-a38EZqyutD2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>run</th>\n",
              "      <th>step</th>\n",
              "      <th>epoch_accuracy</th>\n",
              "      <th>epoch_loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>0</td>\n",
              "      <td>0.966867</td>\n",
              "      <td>0.110196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>1</td>\n",
              "      <td>0.986283</td>\n",
              "      <td>0.042437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>2</td>\n",
              "      <td>0.989333</td>\n",
              "      <td>0.032622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>3</td>\n",
              "      <td>0.991933</td>\n",
              "      <td>0.026121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>adam,run_1/train</td>\n",
              "      <td>4</td>\n",
              "      <td>0.991733</td>\n",
              "      <td>0.024742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>15</td>\n",
              "      <td>0.993800</td>\n",
              "      <td>0.020157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>16</td>\n",
              "      <td>0.993500</td>\n",
              "      <td>0.020212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>17</td>\n",
              "      <td>0.993800</td>\n",
              "      <td>0.020364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>18</td>\n",
              "      <td>0.993100</td>\n",
              "      <td>0.022192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>sgd,run_5/validation</td>\n",
              "      <td>19</td>\n",
              "      <td>0.990800</td>\n",
              "      <td>0.032140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      run  step  epoch_accuracy  epoch_loss\n",
              "0        adam,run_1/train     0        0.966867    0.110196\n",
              "1        adam,run_1/train     1        0.986283    0.042437\n",
              "2        adam,run_1/train     2        0.989333    0.032622\n",
              "3        adam,run_1/train     3        0.991933    0.026121\n",
              "4        adam,run_1/train     4        0.991733    0.024742\n",
              "..                    ...   ...             ...         ...\n",
              "595  sgd,run_5/validation    15        0.993800    0.020157\n",
              "596  sgd,run_5/validation    16        0.993500    0.020212\n",
              "597  sgd,run_5/validation    17        0.993800    0.020364\n",
              "598  sgd,run_5/validation    18        0.993100    0.022192\n",
              "599  sgd,run_5/validation    19        0.990800    0.032140\n",
              "\n",
              "[600 rows x 4 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfw = experiment.get_scalars(pivot=True) \n",
        "dfw"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNnEA5Sywzo0"
      },
      "source": [
        "Notice that instead of a single \"value\" column, the wide-form DataFrame includes the two tags (metrics) as its columns explicitly: `epoch_accuracy` and `epoch_loss`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEenTH7QEfP8"
      },
      "source": [
        "## Saving the DataFrame as CSV\n",
        "\n",
        "`pandas.DataFrame` has good interoperability with [CSV](https://en.wikipedia.org/wiki/Comma-separated_values). You can store it as a local CSV file and load it back later. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "_O4OaPJeckwT"
      },
      "outputs": [],
      "source": [
        "csv_path = '/tmp/tb_experiment_1.csv'\n",
        "dfw.to_csv(csv_path, index=False)\n",
        "dfw_roundtrip = pd.read_csv(csv_path)\n",
        "pd.testing.assert_frame_equal(dfw_roundtrip, dfw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAUURz84q0gB"
      },
      "source": [
        "## Performing custom visualization and statistical analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iKKioeyjARS7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'loss')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Filter the DataFrame to only validation data, which is what the subsequent\n",
        "# analyses and visualization will be focused on.\n",
        "dfw_validation = dfw[dfw.run.str.endswith(\"/validation\")]\n",
        "# Get the optimizer value for each row of the validation DataFrame.\n",
        "optimizer_validation = dfw_validation.run.apply(lambda run: run.split(\",\")[0])\n",
        "\n",
        "plt.figure(figsize=(16, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.lineplot(data=dfw_validation, x=\"step\", y=\"epoch_accuracy\",\n",
        "             hue=optimizer_validation).set_title(\"accuracy\")\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.lineplot(data=dfw_validation, x=\"step\", y=\"epoch_loss\",\n",
        "             hue=optimizer_validation).set_title(\"loss\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CF8g6znMwxN"
      },
      "source": [
        "The plots above show the timecourses of validation accuracy and validation loss. Each curve shows the average across 5 runs under an optimizer type. Thanks to a built-in feature of `seaborn.lineplot()`, each curve also displays ±1 standard deviation around the mean, which gives us a clear sense of the variability in these curves and the significance of the differences among the three optimizer types. This visualization of variability is not yet supported in TensorBoard's GUI.\n",
        "\n",
        "We want to study the hypothesis that the minimum validation loss differs significantly beteen the \"adam\", \"rmsprop\" and \"sgd\" optimizers. So we extract a DataFrame for the minimum validation loss under each of the optimizers.\n",
        "\n",
        "Then we make a boxplot to visualize the difference in the minimum validation losses."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "A4X4XF-GRMBO"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e017c8150>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "adam_min_val_loss = dfw_validation.loc[optimizer_validation==\"adam\", :].groupby(\n",
        "    \"run\", as_index=False).agg({\"epoch_loss\": \"min\"})\n",
        "rmsprop_min_val_loss = dfw_validation.loc[optimizer_validation==\"rmsprop\", :].groupby(\n",
        "    \"run\", as_index=False).agg({\"epoch_loss\": \"min\"})\n",
        "sgd_min_val_loss = dfw_validation.loc[optimizer_validation==\"sgd\", :].groupby(\n",
        "    \"run\", as_index=False).agg({\"epoch_loss\": \"min\"})\n",
        "min_val_loss = pd.concat([adam_min_val_loss, rmsprop_min_val_loss, sgd_min_val_loss])\n",
        "\n",
        "sns.boxplot(data=min_val_loss, y=\"epoch_loss\",\n",
        "            x=min_val_loss.run.apply(lambda run: run.split(\",\")[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "CIWdPx45eSIe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "adam vs. rmsprop: p = 0.0244\n",
            "adam vs. sgd: p = 0.9749\n",
            "rmsprop vs. sgd: p = 0.0135\n"
          ]
        }
      ],
      "source": [
        "# Perform pairwise comparisons between the minimum validation losses\n",
        "# from the three optimizers.\n",
        "_, p_adam_vs_rmsprop = stats.ttest_ind(\n",
        "    adam_min_val_loss[\"epoch_loss\"],\n",
        "    rmsprop_min_val_loss[\"epoch_loss\"]) \n",
        "_, p_adam_vs_sgd = stats.ttest_ind(\n",
        "    adam_min_val_loss[\"epoch_loss\"],\n",
        "    sgd_min_val_loss[\"epoch_loss\"]) \n",
        "_, p_rmsprop_vs_sgd = stats.ttest_ind(\n",
        "    rmsprop_min_val_loss[\"epoch_loss\"],\n",
        "    sgd_min_val_loss[\"epoch_loss\"]) \n",
        "print(\"adam vs. rmsprop: p = %.4f\" % p_adam_vs_rmsprop)\n",
        "print(\"adam vs. sgd: p = %.4f\" % p_adam_vs_sgd)\n",
        "print(\"rmsprop vs. sgd: p = %.4f\" % p_rmsprop_vs_sgd)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcrwpKdNm-nN"
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      "source": [
        "Therefore, at a significance level of 0.05, our analysis confirms our hypothesis that the minimum validation loss is significantly higher (i.e., worse) in the rmsprop optimizer compared to the other two optimizers included in our experiment. \n",
        "\n",
        "In summary, this tutorial provides an example of how to access scalar data as `panda.DataFrame`s from TensorBoard.dev. It demonstrates the kind of flexible and powerful analyses and visualization you can do with the `DataFrame`s."
      ]
    }
  ],
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      "name": "dataframe_api.ipynb",
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